There’s a common character that can be often spotted in many online communities. It’s the “toxic influencer” (sometimes referred as “intellectual bully”). A toxic influencer is a person…
Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity
Play nice! How the internet is trying to design out toxic behaviour
Online abuse can be cruel – but for some tech companies it is an existential threat. Can giants such as Facebook use behavioural psychology and persuasive design to tame the trolls?
Can a video game company tame toxic behaviour?
Scientists are helping to stop antisocial behaviour in the world's most popular online game. The next stop could be a kinder Internet.
The Immortal Myths About Online Abuse
After building online communities for two decades, we’ve learned how to fight abuse. It’s a solvable problem. We just have to stop…
Morally Motivated Networked Harassment as Normative Reinforcement
While online harassment is recognized as a significant problem, most scholarship focuses on descriptions of harassment and its effects. We lack explanations of why people engage in online harassment beyond simple bias or dislike. This article puts forth an explanatory model where networked harassment on social media functions as a mechanism to enforce social order. Drawing from examples of networked harassment taken from qualitative interviews with people who have experienced harassment ( n = 28) and Trust & Safety workers at social platforms ( n = 9), the article builds on Brady, Crockett, and Bavel’s model of moral contagion to explore how moral outrage is used to justify networked harassment on social media. In morally motivated networked harassment, a member of a social network or online community accuses a target of violating their network’s norms, triggering moral outrage. Network members send harassing messages to the target, reinforcing their adherence to the norm and signaling network membership. Frequently, harassment results in the accused self-censoring and thus regulates speech on social media. Neither platforms nor legal regulations protect against this form of harassment. This model explains why people participate in networked harassment and suggests possible interventions to decrease its prevalence.
Digital Social Norm Enforcement: Online Firestorms in Social Media
Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.
Digital Social Norm Enforcement: Online Firestorms in Social Media
Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.
Wikipedia:Long-term abuse
This page summarises a limited number of long term abusers, to assist members of the community who believe they may have cause to report another incident. Note that this page is not a noticeboard. Names should only be added for the most egregious and well-attested cases. Most users here will have been banned, some on multiple occasions.
Lisa Nakamura: The internet is a trash fire. Here's how to fix it.
Life on the internet is real life, and we need to stop pretending that what happens online doesn't matter, says digital media researcher Lisa Nakamura. In this timely talk, she explains the steps we can take to correct the toxic behaviors that have become common online -- and help make the internet a place where we all feel safe.
Anatomy of an internet argument
Social media isn't toxic, most people just haven't figured out the game (yet)
Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity
U.S. political discourse seems to have fissioned into discrete bubbles, each reflecting its own distorted image of the world. Many blame machine-learning algorithms that purportedly maximize “engagement”—serving up content that keeps YouTube or Facebook users watching videos or scrolling through their feeds—for radicalizing users or strengthening their partisanship. Sociologist Shoshana Zuboff15 even argues that “surveillance capitalism” uses optimized algorithmic feedback for “automated behavioral modification” at scale, writing the “music” that users then “dance” to.
Bias, Skew, and Search Engines Are Sufficient to Explain Online Toxicity
U.S. political discourse seems to have fissioned into discrete bubbles, each reflecting its own distorted image of the world. Many blame machine-learning algorithms that purportedly maximize “engagement”—serving up content that keeps YouTube or Facebook users watching videos or scrolling through their feeds—for radicalizing users or strengthening their partisanship. Sociologist Shoshana Zuboff15 even argues that “surveillance capitalism” uses optimized algorithmic feedback for “automated behavioral modification” at scale, writing the “music” that users then “dance” to.
The Noisy Room
Only 3% of social media users post toxic content — but Americans estimate 43%. An interactive essay.
Frontiers | Cyber-mobbing, technology-facilitated abuse and online violence
This article collection will explore the pressing issues of cyber-mobbing, technology-facilitated abuse and online violence, including online abuse, stalking...
Beyond the Pentakill: 21st Century Competition
There’s been a lot of discussion about how to prevent toxic behavior in players of online competitive games. Today I’d like to suggest a different approach.
The Slander Industry (Published 2021)
Who makes money from destroying reputations online?
